Covid-19 risk and illness assessment method

ABSTRACT

A method, system, and/or apparatus for automatically monitoring for possible infection or other physical health concerns, such as from Covid-19. The method or implementing software application uses or relies upon location information available on the mobile device from any source, such as cell phone usage and/or other device applications. The method and system automatically uses and/or learns user location and activity patterns and determines and infection risk or illness-based deviation that can be communicated as a warning to community members.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 17/038,064, filed on 30 Sep. 2020, which is a continuation ofU.S. patent application Ser. No. 16/432,373, filed on 5 Jun. 2019, whichis a continuation of U.S. patent application Ser. No. 16/046,590, filedon 26 Jul. 2018, which is a continuation of U.S. patent application Ser.No. 15/291,819, filed 12 Oct. 2016, which is a continuation of U.S.patent application Ser. No. 14/848,881, filed 9 Sep. 2015, which is acontinuation-in-part of U.S. patent application Ser. No. 14/751,399,filed 26 Jun. 2015, which is a continuation-in-part of each of: U.S.patent application Ser. No. 14/270,534, filed 6 May 2014, U.S. patentapplication Ser. No. 14/455,279, filed 8 Aug. 2014, and U.S. patentapplication Ser. No. 14/455,297, filed 8 Aug. 2014; each of which is acontinuation-in-part of each of U.S. patent application Ser. No.14/051,071, filed on 10 Oct. 2013, and U.S. patent application Ser. No.14/051,089, filed on 10 Oct. 2013. The co-pending parent applicationsare hereby incorporated by reference herein in their entirety and aremade a part hereof, including but not limited to those portions whichspecifically appear hereinafter.

FIELD OF THE INVENTION

This invention relates generally to automated health monitoring, andmore particularly, to a method, system, and apparatus that automaticallydetermines infection risk, such as during the current Covid-19 pandemic,and/or detects and informs of possible medical issues related to theinfection.

BACKGROUND OF THE INVENTION

The current Covid-19 pandemic has magnified a need for monitoringmethods. Contract tracing relies on a person to self-identify asinfected, and suffers from concerns of information leaks. Testing andtreatment can be difficult due to shortages, delays, etc.

Prevention is better than treatment. There is a need for passivemonitoring based systems and methods that indicate likelihood of viraland/or bacterial exposure, such as due to contact with a specificindividual.

Social media systems have permeated daily life. Information iscollected, organized, and disseminated worldwide via informationalcollection and dissemination, micro-blogging and blogging services.Other social media are mobile and positional in nature and can bereferred to as Mobile Positional Social Media (MPSM). As these systemsfocus on locations, mobile device implementations permeate the space.That said, however, while MPSM implementations are targeted to primarilyexecute on mobile devices, such as but not limited to smart-phones(e.g., Apple's iPhone, Google's Android), tablets (e.g., Apple's iPad,HP TouchPad), and laptop computers, they often support implementationsfor non-mobile environments such as but not limited to desktops andworkstations, and large scale compute farms and cloud computing servers.

One limitation of MPSM systems is their reliance on global positioningsystems (GPS). The use of GPS devices does typically simplify locationtracking implementation; however, this comes at a significant energycost. Since a significant portion of MPSM systems usage is via mobiledevices, reducing energy consumption is critical.

Another limitation of current MPSM systems, similar to contact tracing,is their reliance on active users identifying their location and/ortheir activity at the location. Another limitation of current MPSMsystems is the limited modes of informational guidance provided to theuser. For example, no reminders or instructional commenting is provided.That is, users are not reminded of activities that fit their givenlocation and context in a push manner; rather, user inquiry of locallyavailable options is needed. Ideally, given location and context usersare proactively pushed information that is immediately relevant to them.Additionally, activities that are nearby to their current location orwill become available can likewise be identified.

The use of smartphone technology for medical applications is increasing.In August 2014, the Food and Drug Administration (FDA) cleared asmartphone device, AliveCor, that detects atrial fibrillation, apotential warning sign for heart failure and stroke. Additionally, AppleCorporation, in support of medical smartphone technology, recentlydeveloped an open-source tool kit to aid in the authoring of medicalresearch applications. Also known in the art is the use of activity andtrend detection using smartphone technology for medical applications.Recently, an Android based application called StudentLife was introducedat Dartmouth College. This application monitors smartphone use (e.g.,text messaging, e-mail traffic, etc.) as well as additional activity(e.g., mobility and sleep patterns) to infer state of being. Automaticsensed, as well as interactive user probing, data were collected todetermine potential stress.

There is thus a continuing need for and interest in improved mobiledevice-based systems and applications for medical applications, andparticularly for infectious disease control and monitoring.

SUMMARY OF THE INVENTION

This invention provides a method, system, and apparatus, such asembodied in a MPSM or other software application that automaticallydetermines infection risks for individuals during an epidemic orpandemic, such as Covid-19, and/or detects abnormal behavior for thepurpose of identifying health concerns related to the infection. Themethod and system of this invention monitor, such as via mobile devices,locations, activities, and/or in-person contacts for users of theinvention. This information is then used to determine and assign a riskassessment to the user, which can be used by others, including family,friends, and even employers or other third parties to make decisions onwhether to have contact with the users. The invention can conceal thespecific activities, thereby avoiding privacy issues, but still give ageneral risk warning indication, such as low-high, or a numerical orcolor version thereof.

In embodiments of this invention, the risk assessment is obtainedmathematically by allocating a predetermined value to various locationsand activities. For example, the method and system can use currentinfection information for a region (e.g., state, county, city, etc.) toset a risk metric for a location within that region. Likewise,particularly for the Covid-19 pandemic, government and healthcareagencies have provided typical locations (e.g., bars) and activities(e.g., eating indoors at restaurants) with warning scales, where higherrisk activities are ones that are more likely to result in an exposureto the SARS-CoV-2 virus. Similarly, in-person contact with others can beused to further scale the risk assessment, even including considerationof a calculated risk assessment of the in-person contact if known.

The invention operates on information obtained about a user's travelsand activities. This information can be determined using any knownmonitoring method, such as mobile phone tracking. In embodiments of thisinvention, the method and system can automatically monitor and determinelocations and/or activities of a user, thereby learning activitypatterns for the user. The application learns user activity over time,with the learning based upon user locations and/or context. The presentinvention generally provides and uses methods and applications for aMPSM that automatically understands and informs the “who, what, when,where, and/or how” of a user and the user's community. For example, whoare the user and their community with, what are the user and theircommunity doing, where geographically are the user and their community,when are, and historically when were, the user and their community doingthis, and/or how can users' behaviors be modified? While the locationand activity learning can be on-going, the risk assessment can bedetermined for a particular timeframe of interest, such as apredetermined incubation or latency period of a contagion or pathogen(e.g., two weeks before a scheduled meeting).

The invention relies on the automatically learned location and activitypatterns of the individual user and/or her/his community members todetect any abnormal behavior, such as a significant deviation from thelearned activity pattern. Such deviation in behavior can occur for manyreasons, including those of medical (e.g., Covid-19) concern, and canserve as an early indicator of potential illness, or as a further riskassessment metric (e.g., Bill went outside his established “bubble offriends and/or activities, thereby increasing risk).

The invention includes a method of determining medical conditions ofusers participating in a social networking service. The method isexecuted by a computer system and includes: automatically monitoringdestinations and user activities of a first user via a first electronicdevice of the first user; automatically learning activity patterns forthe first user; automatically determining a deviation in the learnedactivity patterns; automatically analyzing the deviation to identify asignificance of the deviation; automatically correlating thesignificance of the deviation to a possible medical condition; andautomatically alerting the first user via the first electronic device ora second user via a second electronic device of the possible medicalcondition. Embodiments of this invention incorporate an inference engineto automatically detect, analyze, and/or correlate the deviation.

Embodiments of this invention include a method of determining infectionrisks for individuals during an epidemic or pandemic, such as Covid-19.The method is executed by a computer system and includes steps of:automatically determining positional destinations of a user using afirst mobile electronic device of the user; automatically determiningcontext information about each of the positional destinations withoutinput by the user; automatically deducing as user information a locationtype and/or user activity of each of the positional destinations fromthe context information; assigning a user risk assessment to the useraccording to locations and/or activities of the user during apredetermined timeframe; and providing the user risk assessment to acommunity member, such as before an in-person meeting, via a communitymember electronic device.

The predetermined timeframe can be determined from or by a predeterminedincubation or latency period of a contagion or pathogen. For example,the predetermined period can be a two-week period for possibleSARS-CoV-2 infection. The period can be determined, and adjusted,depending on need, and any particular pathogen.

The user risk assessment can be a compilation of a plurality of riskmetrics determined for the locations and/or activities during thepredetermined time period, and each of the risk metrics is determinedfrom a predetermined assessment score for each of the location and anyactivity performed at the location, as a function of time at thelocation and/or performing the activity. The invention can include, forexample, steps of: determining a risk metric for each of the locationsand/or activities for the user, to provide the plurality of risk metricsduring the predetermined timeframe, wherein the determining the riskmetric for the each of the locations and/or activities comprisescomparing a location and/or an activity to a predetermined risk scale;determining a corresponding user participation time for the each of thelocations and/or activities; scaling the risk metric for the each of thelocations and/or activities according to the corresponding userparticipation time; increasing any risk metric of the user riskassessment upon an in-person contact at the each of the locations and/oractivities with a person having a negative (e.g., high or otherwiseundesirable) risk assessment; and computing the user risk assessmentfrom the plurality of risk metrics. As used herein, a “negative” riskassessment generally refers to a greater chance of being infected, basedupon methods and scales according to this invention.

The invention further includes a method of determining infection risksand/or illness for individuals during an epidemic or pandemic, such asCovid-19. The method executed by a computer system and includes:automatically determining and monitoring positional destinations of afirst user using a first mobile electronic device of the user;automatically determining context information about each of thepositional destinations without input by the first user; automaticallydeducing as user information a location type and/or user activity ofeach of the positional destinations from the context information;automatically learning activity patterns for the user from the userinformation; assigning a user risk assessment to the first useraccording to locations and/or activities of the user during apredetermined timeframe; providing the user risk assessment to acommunity member via a community member electronic device; automaticallydetermining a decrease deviation in the learned activity patterns duringor after the predetermined timeframe from further monitoring of furtheruser locations or further user activities; automatically analyzing thedecrease deviation to identify a significance of the deviation, whereinanalyzing the decrease deviation comprises: correlating the deviation toa current location of the first user to identify a temporary decrease inthe learned activity patterns as a function of the current location notallowing for the learned activity patterns, and identifying words and/orideas from sent or received messages via the first mobile electronicdevice to identify an explanation for the deviation; automaticallycorrelating the significance of the decrease deviation to a possibleCovid-19 condition; and automatically alerting the first user via thefirst mobile electronic device or a second user via a second electronicdevice of the possible Covid-19 condition.

A method of determining infection risks and/or for individuals during anepidemic or pandemic, such as Covid-19, according to this invention caninclude steps of: automatically monitoring destinations and useractivities performed at the destinations of a first user via a firstelectronic device of the first user; receiving user informationcomprising a first user activity performed at each of the destinationsupon a user arriving at the each of the destinations; automaticallylearning activity patterns for the first user activity by automaticallyassociating the user information with the at least one of thedestinations, automatically determining a user activity context for thefirst user activity, and automatically comparing a further context ofeach of a plurality of further user arrivals to the at least one of thedestinations to the user activity context, wherein the activity patternscomprise an eating pattern or an exercise pattern, at a location or withone or more community members; automatically updating contextinformation associated with the at least one destination from thefurther context of each of the further user arrivals; automaticallyidentifying that the first user is participating in the first useractivity upon future user arrivals at the at least one destination as afunction of comparing the context information to a future context ofeach of the future user arrivals, without receiving any manually enteredconfirmation or manually entered additional user information; assigninga user risk assessment to the user according to locations and/oractivities of the user during a predetermined timeframe, wherein thepredetermined timeframe is at least a predetermined incubation orlatency period of a contagion or pathogen; providing the user riskassessment to a community member via a community member electronicdevice before an in-person meeting with the user; automaticallydetermining a deviation in the learned activity patterns during or afterthe predetermined timeframe from further monitoring of further userlocations or further user activities; automatically analyzing thedeviation to identify a significance of the deviation, wherein analyzingthe deviation comprises: correlating the deviation to a current locationof the first user to identify a temporary change in the learned activitypatterns as a function of the current location not allowing for thefirst user activity at the at least one of the destinations, and/oridentifying words and/or ideas from sent or received messages via thefirst electronic device to identify an explanation for the deviation;automatically correlating the significance of the deviation to apossible Covid-19 condition; and automatically alerting the first uservia the first electronic device or a second user via a second electronicdevice of the possible Covid-19 condition.

In embodiments of this invention, the automatic monitoring ofdestinations and user activities of a first user via a first electronicdevice of the first user includes: automatically determining acorresponding context for each of the user activities at a first uservisit to each of the destinations; automatically tagging each of thedestinations with a corresponding one of the user activities andcorresponding context information, and storing the tagged location in alocation database; automatically updating the context information of thetagged location for each of a plurality of automatically determinedfurther user visits at the destinations from the further context of eachof the further user visits; and automatically associating the first useractivity with corresponding destinations and the corresponding contextinformation. Each of the first context, the second context, the furthercontext, and the context information can include at least one of: a timeof day, a day of a week, a calendar date, a preceding user activity tothe user visits, a weather condition, people accompanying the user, orcommunity member activity bias information for the destination.

The invention includes a method of automated learning location andactivity patterns for a user and automated determination of medicalconcerns, such as through a social networking service. The method isexecuted by a software application stored and executed on one or morecomputers or data processor systems, such as a mobile device (e.g.,phone, tablet, or laptop) and/or an application server (such as forconnecting user communities). In one embodiment the method includesreceiving user information about a destination, automaticallyassociating the user information with the destination, learning activitypatterns for a user, automatically sharing the user information in thesocial networking service upon further user arrivals at the destinationprior to receiving any additional user information, automaticallymonitoring for or detecting deviations in the expected activity pattern,and/or automatically determining and reporting that the deviation issignificant enough to warrant a health (e.g., infection) concern for theuser.

The invention also includes a method and supporting system fordetermining potentially unhealthy or dangerous risks and/or changes inactivity patterns of users participating in a social networking service.The method is executed by a computer system and includes steps of:automatically and either periodically or continually tracking locationsof member electronic devices of a plurality of members of a social mediacommunity of a first user; automatically displaying location informationfor each of the plurality of members on a first user electronic device;automatically determining by analysis a significant deviation in anactivity pattern; automatically determining and communicating anincreased risk of infection through a respective user electronic deviceand/or automatically communicating the occurrence of the triggeringcondition to the first user or a second user through a respective userelectronic device. The analysis of the change in activity patterndesirably includes automatically determining context information aboutthe location information for the user without manual input therefrom;automatically deducing as user information a location type and/or otherpotential reason for the change in activity pattern (e.g., vacation,working overtime, weather/allergy conditions, or mild injury).

The electronic message can be delivered to the user herself/himself onher/his mobile device (e.g., the message creating device or anothermobile device of the user) or to one or more other user device(s), suchas a close friend, family member, and/or a medical professional. Inother embodiments of this invention, the second user is someone expectedto meet with the first user, such as an employer or co-worker, a client,or a Covid-bubble friend.

The invention still further includes a computer server for providing atracking and/or social networking service, such as operating the methodsdiscussed herein. The computer server of one embodiment of thisinvention includes a tagging module configured to correlate userinformation to a user destination, a database module configured to storeuser information including user locations and user activities at theuser locations, an inference engine, and a communication moduleconfigured to automatically share medical concerns and/or a useractivity in the social networking service upon further user arrivals ata corresponding one of the user locations. The computer system can alsoinclude an association module configured to associate the user activitywith the corresponding one of the user locations.

In embodiments of this invention, the system or application identifieslocations, and over time, automatically “checks-in” not only thelocations but what the locations imply in terms of potential activitiesof the user. That is, given a location and a user, the system desirablysuggests what activity or activities the user typically partakes at thatlocation. For example, if a user frequents a location in “Potomac,” thislocation might be identified as “parents' home.” Furthermore, at thishome, a variety of activities might be common such as: “visitingparents,” “drinking tea,” “eating lunch,” or “sampling wine.” Inembodiments of this invention, each time the user appears at thatlocation, based on context, defined by elements of or surrounding theactivity such as but not limited to time of day, day of week,immediately preceding activities, weather, surrounding people, etc., aset of likely occurring activities are identified. The user can beprompted with a list from which to select a subset of these activitiesor to identify a new activity. The invention can also include ranking auser's potential suggested activity based on context and presenting thatranked list to the user, or the user's community. The inventiongenerally provides a learning component that can allow the manual inputsto become automatic prompts, which can become automatically issuednotifications for the location based upon the context. The prompts canbe issued through any known format, such as an application alert on thedevice or a text message to the user. The invention also supports theuser changing activities for a given location at any time, and/or userimplemented delay of the notification of a user's location or activity.

The invention can include the incorporation or creation of usercommunities and sub-communities, with such communities andsub-communities sharing information. Embodiments of the inventioninclude automatically identifying a user's location and activity, anddesirably notifying that user's community of that user's location.Particular embodiments of this invention provide one or more additionalcommunity functionalities including, without limitation, automaticallyidentifying a user's activity and notifying that user's community ofthat user's activity, commenting on user activity and location report byuser or community—with multiple and multimedia comments supported,supporting the “liking” of user activity by the community, supportingthe user tagging of location, activity, or the pairing of location andidentity—tagging can be textual or via any multimedia means, correlatingthe individual user's activity with the ongoing activity of otherswithin the community, and/or correlating the individual user's activitywith the past activities of others within the community.

One embodiment of this invention provides a method of and system forautomated determining of locations and/or activities of a userparticipating in a social networking service. The method is executed bya MPSM computer system and automatically determines a positionaldestination of a user, automatically deduces as user information alocation type and/or user activity of the positional destination, andautomatically shares the user information as instructed in the socialnetworking service. Deducing the user information is based upon contextinformation about the positional destination, desirably with minimal orno input by the user. The context can include, without limitation,time-dependent information, past and/or current associated userinformation, past user and/or community information about the location,and/or third party information. The context can be used to at leastreduce location types and/or user activities, for example, as a functionof the past location type and/or user activity of the positionaldestination for a given time period.

Another limitation of current MPSM systems is their lack of individualand community activity summarization capability. That is, summary of thelocal user and community member activities and time durations are notavailable, neither to the local user nor to their community. Thissummarization can range from simple statistical aggregation to advancedcorrelations as derived by known techniques in the art. In embodimentsof this invention, users are provided with summaries of their locations,durations at these locations, and activities at these locations.Furthermore, at the discretion of the local user, these summaries aremade available to their community members. Particular embodiments ofthis invention provide one or more additional summarizationfunctionalities including, without limitation, maintaining a history ofuser locations, activities, or combination thereof, correlating theindividual user's activity with the past activities of the user,correlating the individual user's activity with the expected learnedfuture activities of self, data mining behavioral patterns andsuggesting alternatives to avoid obstacles, providing statisticalaggregation of locations visited, providing derived summarization oflocations visited, providing statistical aggregation of activities,providing derived summarization of activities, providing statisticalaggregation and/or derived summarization of individuals (such ascommunity members) encountered during a time period.

Other objects and advantages will be apparent to those skilled in theart from the following detailed description taken in conjunction withthe appended claims and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a representative area of a user of one embodiment of thisinvention.

FIG. 2 illustrates geofences surrounding a current reading and itsimmediate neighbors according to one embodiment of this invention.

FIG. 3 illustrates the determination of a location via intersectingcircles according to one embodiment of this invention.

FIG. 4 illustrates the processing flow employed to identify an arrivalaccording to one embodiment of this invention.

FIG. 5 illustrates example Covid-19 cases for all 23 Maryland countiesand the City of Baltimore.

FIG. 6 shows exemplary Covid-19 risk assessment scores for activitytypes.

FIG. 7 shows database records of locations, durations, and types ofactivity for community members under consideration in a providedexample.

FIG. 8 illustrates an exemplary summary of top activities determinedaccording to embodiments of this invention.

FIG. 9 illustrates an exemplary summary of selected activities acrossmultiple periods according to embodiments of this invention.

FIG. 10 illustrates an exemplary summary of top localities determinedaccording to embodiments of this invention.

FIG. 11 illustrates an exemplary summary of selected locality acrossmultiple periods determined according to embodiments of this invention.

FIG. 12 illustrates an exemplary summary of top community memberinteractions determined according to embodiments of this invention.

FIG. 13 illustrates an exemplary summary of selected community memberinteractions across multiple periods determined according to embodimentsof this invention.

FIG. 14 illustrates an exemplary monitoring with a potential issuedetected according to one embodiment of this invention.

FIG. 15 illustrates smartphone and fitness tracker or other biosensordevice interaction according to one embodiment of this invention.

DETAILED DESCRIPTION OF THE INVENTION

This invention provides a method, system, and/or apparatus, such asembodied in a MPSM or other software application that automaticallydetermines infection risks, and possible illness/symptoms, forindividuals during an epidemic or pandemic, such as Covid-19. The methodand system of this invention monitor, such as via mobile devices,locations, activities, and/or in-person contacts for users of theinvention. This information is then used to determine and assign a riskassessment to the user, which can be used by others, including family,friends, and even employers or other third parties to make decisions onwhether to have contact with this user. The MPSM technology and methodof this invention can also be paired with other electronic monitoringsystems, such as electronic pulse, body temperature, or other fitnessrelated monitors, particularly wearable devices, via wirelesscommunication.

The automatic and continual determination of locations and/or activitiesof users participating in a social networking service allows forlearning behavior patterns, and more particularly activity patterns,such as location visits, activities performed at the locations, and anycommunity presence at those activities. As a brief example, uponrepeated Friday morning tennis at the club with John, the system learnsthat the user is playing tennis on Friday mornings if the system detectsthat the user is at the club and/or the user is with John. If the userunwisely continues these riskier activities against health expert adviceduring an epidemic or pandemic, then the user's risk of infection isincreased and can be quantified and reported by this invention, evenwithout specifically saying John played tennis. The user's riskassessment can be automatically provided to a community member (e.g.,family member, friend, employer, etc.) via a community member electronicdevice. The invention also merges this technology with health caremonitoring, by using the learned patterns to analyze and detect behaviorpatterns that correlate to a potential health or medical concern, suchas illness from an infections. Using the example above, if the userstopped playing tennis with John, and/or stopped going to the club orperforming any exercise, then there may be a potential health (e.g.,infection) concern.

As such, the invention relies on automatically learned location andactivity patterns of an individual and her/his community members todetect abnormal behavior that could indicate illness or infection. Suchdeviation in behavior can occur for many reasons, including those ofmedical concern and can serve as an early indicator of potentialsmoldering conditions. Without intending to be so limited, the inventionis described below for brevity with reference to a non-limitingexemplary condition, namely Covid-19. It is, however, within the scopeof this invention, to detect, via MPSM technology, other ailments thatexhibit symptoms detectable using inference from automatically learnedlocation and activity, including other mental health issues, such asaddictions or bi-polar issues, and/or physical health concerns, such asheart health concerns and weight-related concerns. The invention canalso be used for monitoring behavior patterns to determine if someone isnot taking medication, or otherwise following a treatment plan. The MPSMtechnology and method of this invention can also be paired with otherelectronic monitoring systems, such as electronic pulse or other fitnessrelated monitors, particularly wearable devices, via wirelesscommunication.

Embodiments of this invention warn of potential viral and/or bacterialexposure due to contact with a specific community member. Such warningsaid in combating spread during epidemic and pandemic outbreaks such as,but not limited to, Covid-19, and monitoring/contract tracing newlocations of viral variants thereof. As disclosed, the inventionpassively records, for each community member, locations resided orvisited, duration at the given locations, and activities involved ateach location. Note that it is also within the scope of the invention torecord only selected activities, namely recording only those activitiesand locations of interest. Using this information, embodiments of thisinvention determine a categorical degree of risk of infection, forexample, low, medium and high, that visiting locations, performingactivities, and/or physically interacting with a particular communitymember poses based on that member's prior geographical presence andbehavioral patterns.

As such, the invention relies on automatically determined and/or learnedlocation and activity patterns of an individual and her/his communitymembers to determine infection or illness risk. In embodiments of thisinvention, the user's risk assessment is a compilation of a plurality ofrisk metrics determined for the locations and/or activities using amobile device or other information, during a predetermined time period.A risk metric is determined for each of the locations and/or activitiesfor the user, to provide a plurality of risk metrics during thepredetermined timeframe for the computation.

In embodiments of this invention, the application learns user activityover time, with the learning based upon user locations and/or context.The application can learn through automatically determining activitiesat locations based upon known context information and past contextinformation for the location. The invention further includes energysaving location methods for the mobile device that can be used to moreefficiently allow the location and social media aspects of the inventionto be implemented on a mobile device. The method and application can beused for any suitable function, such as a safety and/or reminder serves,and is particularly useful for use in social media applications. Theinvention will be described below with implementation in a MPSM system,and particularly with an MPSM application that learns user activity overtime, with the learning based upon user locations and/or context.

The method and system of this invention is mobile and positional innature. Such systems, like many other systems originally developed onone type of computing platform but migrated to another, operate not onlyon mobile environments. That is, while MPSM implementations are targetedto primarily execute on mobile devices, such as but not limited tosmart-phones, tablets, and/or laptops, they often support implementationfor non-mobile environments such as but not limited to desktops andworkstations, servers, and large scale compute farms and cloud computingservers. The invention will be described below with a mobile device,such as smart phone having cell service, a GPS system, and access to theInternet via WiFi.

The method and system of this invention is desirably executed orimplemented on and/or through a mobile device computing platform. Suchcomputing platforms generally include a processor, a recordable medium,an input/output (I/O) device, and a network interface capable ofconnecting either directly or indirectly to the Internet. The mobiledevice executes over a networked environment, a non-limiting exampleshown in FIG. 1. The mobile device is connected, either directly orindirectly, using any of the many techniques and technologies known inthe art, over a network, to back-end system or systems,itself/themselves computing devices. The mobile device can connect witha remote server, shown in FIG. 1 as server 38, to store and/or accessuser or community information.

MPSM systems are used to support users remaining socially aware of theircommunity. That is, their primary usage typically is to actively monitorthe location and activity of family members, friends, colleagues, andgenerally others within one's community. Communities can be partitionedinto sub-communities where the union of the sub-communities forms theuser's community. The sub-communities may or may not overlap. Thepartitioning of communities into sub-communities is beneficial insupporting specialized applications. For example, while a user mighthave general interest in the location and activity of all of theircommunity members, they might be particularly interested in the locationand activity of those who might be suddenly in need of assistance.

Regardless of the community size, besides tracking users to potentiallyprovide immediate assistance, medical environments that supportstate-of-being can capitalize on MPSM systems. State-of-beingapplications can detect abnormal patterns in a user's behavior orphysical presence. By learning typical behavior of an individualregularly using a MPSM system according to embodiments of thisinvention, abnormality in behavior can be detected, and an alarm issued.It is within the scope of this invention to additionally incorporatedata and information from health monitoring applications known in theart that are likewise resident on the mobile device to supportstate-of-being applications. Similarly, in community activities thatrequire continuous monitoring and coordination, such as but not limitedto an emergency response team or a neighborhood watch or othersurveillance efforts, MPSM systems according to this invention canprovide the necessary infrastructure to support the neededsynchronization.

The creation of a community can include the issuing of invitations. Aninvitation is a request by a user A of another user B to allow theinviting user, user A, to track the activities of the invited user, userB, and vice versa. If the invited user accepts, the inviting and invitedusers form a community.

A community is relevant to only that user which formed it. That is,different users have different communities. A community is a grouping ofinvited (referred to as remote) users by the inviting (referred to aslocal) user. A local user can partition or merge a community, thusforming a sub-community or a parent community, respectively. Forexample, consider 5 users: Bob, Sam, Sally, Alice, and Susan. Bob caninvite Sam, Sally, and Alice, thus forming his user community. Bob canlikewise partition his community into a sub-community consisting of onlySam and Sally. Sally can invite Susan. Thus, Sally's community wouldinclude Bob (via his invitation) as well as Susan. If no additionalinvites occurred, Sam's and Alice's respective communities would onlyinclude Bob (each via Bob's invitation), while Susan's community wouldonly include Sally (via Sally's invitation).

Providing users with the opportunity to expand their communities in aconvenient manner is advantageous. Such expansion can seamlessly beaccommodated by including users listed in a user's contact lists eitheras a whole or selectively into their community. Contact lists include,but are not limited to, users listed in a user's local address book,e-mail contact list, Twitter follow list, LinkedIn connections list,and/or Facebook friends list. By incorporating users listed in a user'scontact list, the user's community is expanded without effort. Note,however, that selected inclusion can be supported; thus enablingcommunity growth without unnecessarily over-expanding the community.That is, entries from the contact list can be included in their entiretyand the user can selectively remove those entries which s/he wishes tobe excluded from the community. Similarly, entries from the contact listcan be selectively added.

Users are identified by their account identifier. To use MPSM a useraccount is created. User accounts generally require a user login, whichis a unique user identifier, and a password or equivalent. After havingcreated an account, a user can log in. Initially, the local user doesnot have a community. In embodiments of this invention, over time, themethod and application tracks the activities and location of the localuser. Should the local user establish a community as aforementioneddescribed, the community members will likewise be tracked. Local usersreceive notifications of the location and activities of their communitymembers. Once logged in, the local user can select to activate ordeactivate self and community tracking and notification. If notoverwritten, default settings are used.

Whenever logged in and tracking is enabled, a user's location andactivity is tracked. That is, a user periodically records their locationand/or activity. Locations are tagged by name. Names can be but are notlimited to the following schemes: physical (e.g., 123 Oak St.), absolute(e.g., Acme Coffee), and/or relative (e.g., my work office), orproximity (e.g., two miles from home). Activities are typically events.These events might be common to the entire community such as: “drinkingcoffee,” “eating lunch,” “sampling wine,” “working from home,”“commuting,” etc., to more specific to a local user such as “restoringcar” or “driving to lake home.” Multiple activities can occursimultaneously. Users can change their activities at any time.

Unless preloaded or derived from an external source, such as but notlimited to a location database, initially, all locations and activitiesare unknown. Local users must record all such location-activitycombinations, i.e., a local user must name or tag the location and theassociated activity. A list of activities common to the local user'scommunity can be provided. This community activity list can be rankedeither arbitrarily (randomly), according to most recently used, mostfrequently used, relevance to location, alphabetically, etc. Eventually,an activity list specific to the local user is learned. This local useractivity list can be displayed to the local user either individually,along with the community list, or merged with the community list. Again,any of these lists can be ranked as previously mentioned.

FIG. 1 illustrates a representative area 30 to demonstrate a method ofand application for locations and/or activities of a user participatingin a social networking service. The area 30 is shown as a cellularcommunication network including a plurality of cells 32 each disposedaround a cellular communication antennae or base station 36. Within thearea are a plurality of destinations each shown as including a WiFiInternet connection. The local user has one or more electronic devices,such as a mobile device that communicates with a remote server 38 viathe cellular network and/or the WiFi connections. As will be appreciatedthe methods and applications of this invention can operate within anysuitable size and configuration of the communication area, depending onwhat the user encounters.

Destination 40 is the home of the user. The user commutes to office 42for work on most business days. On the way the user typically stops atthe coffee shop 41. For lunch on most days, the user visits restaurant43, but on Wednesdays the user typically meets a second user for lunchat restaurant 44.

At each destination 40-44, the user enters user information about thedestination. The application and computer system that receives the userinformation automatically associates the user information with thedestination, and stores the user information in a locations database,such as on the device and/or at server 38. The destination desirably isdetermined automatically and tagged with the user information, such as alocation name of the destination and/or the user activity beingperformed at the destination. For example, destination 40 can be taggedas “home” and likely has numerous activities associated with it. Thedestination 41 can be tagged as its establishment name “Acme Coffee” orsimply “coffee shop” and associated with the user activity of “buyingcoffee” or “latte time.” The manually entered user information can thenbe automatically shared to the user's community in a social networkingservice. Similar user information is received for the other destinations42-44. The user information desirably includes any other informationabout the location or activity, whether manually entered orautomatically determined, such as the time of the visit or activity.Some destinations, such as home or work will likely have multiple useractivities over a period of time, such as “coffee break,” “meetingtime,” and/or “quitting time.”

The computer system receives user information and associates the userinformation with the corresponding destination for multiple visits toeach of the destinations 40-44. The computer system begins learning thelocations and user activities. In embodiments of this invention, theuser can be automatically prompted for confirmation of the userinformation upon arriving at a destination to confirm the locationand/or user activity. For example, the user can be provided with anautomatically generated list of previously entered user activities forthe destination upon arrival, thereby promoting efficient collection ofinformation. The items on the list can be listed in an order based upona particular ranking, such as number of times entered previously orbased upon a context, such as what activity is likely being performed ata particular time of a particular day.

Over time, the computer system learns the user information and beginsautomatically associating and identifying at least some user activitiesfor corresponding locations. As will be appreciated, the automaticidentifying of activities at locations will likely occur at differentrates for different activities and locations, with some locations havingfewer activities and/or more frequent visits than others. In preferredembodiments of this invention, the system automatically shares the userinformation in a social networking service upon automatically detectingfurther user arrivals at the destination. The automatic sharing of userlocations and/or activities desirably occurs upon the user's arrival atthe location, or at a particular time at the location. As such theinvention includes an automatic detection of the user's arrival at adestination. The automatic sharing desirably operates without useraction and prior to receiving any additional user information for thedestination.

As an example, the user may typically purchase lunch at destination 43,but on Wednesdays typically goes to lunch with a friend or spouse atdestination 44. The lunch routines of the user, and particularly theWednesday lunch routine, can be learned by the system and automaticallyshared to the user's community upon the system automatically determiningarrival, without manually input from the user. If the user is havinglunch with a community member, then the system can automaticallydetermine that both users are at the same location together toautomatically recognize and confirm the lunch activity, and proceed toautomatically share the information for both user's to their respectivecommunities. If the user deviates from a routine, the system can knowthis, and refrain from sharing the typical destination, by the mobiledevice detecting a different location than the typical routinedestination.

In embodiments of this invention, learning is accomplished by any knownmachine learning, data mining, and/or statistical techniques known inthe art. Supervised, semi-supervised, and/or unsupervised approaches canbe used, including, but not limited to Naïve Bayes, Neural Networks,Support Vector Machine, and/or Associating Mining based techniques.

The invention desirably records all posted locations and activities.Throughout use, the disclosed invention learns the correspondinglocations and the set of associated activities. More so, via commentsmade by the local user and by the local user's communities, theimportance of the activities can be learned, such as for the promptingdiscussed above. Importance can be either local user or communitybiased. Additionally, importance can be biased by context. For example,community members as a whole might prefer “eating steak,” “eatingpizza,” and “eating sushi,” in that respective order. On the other hand,a local user might only eat sushi. Thus, local user bias will yield“eating sushi” only, while community bias will suggest “eating steak,”“eating pizza,” and “eating sushi,” in that respective order.

In embodiments of this invention, locations are named according to anaming convention. Regardless of the naming convention used, a locationis a physical geographical position. More so, physical geographiclocations associate properties that can vary with or be dependent oncontext, namely time and date (hours, day of week, calendar date, etc.),users involved, and their relationships to each other, etc. This contextcan affect the associated location name or activity.

A common scheme that can be used to at least assist in identifying aphysical geographical location is via the use of geocoding. Geocoding isthe representation of a physical location via the pairing of latitudinaland longitudinal coordinates commonly referred to as a lat-long pair.Global Positioning Systems (GPS) can also determine a physical positioncoordinated via the triangulation of satellite transmissions. TypicallyGPS devices derive lat-long pairs which are made available to a varietyof applications, often via map displays. GPS economics, accuracy, andsimplicity of use resulted in their wide appeal and commercial success.Their continuous use in mobile devices is problematic, however, as theyare energy intensive and rapidly drain the battery. Thus, alternativemeans or approaches to detect locations are desired.

Embodiments of this invention, as discussed above in FIG. 1, use or relyupon cell coordinates. When mobile devices communicate with a celltower, they send their cell coordinates. These coordinates are recordedby the cell provider and are typically not publicly known. The cellphone or, in this case, the mobile device supporting the positionalsocial media system, however, is aware of their coordinates. Thus, thedevice can store the cell coordinate position and automaticallyassociate that cell coordinate with the location name provided by thelocal user. Over time, a location database of cell coordinate and namedlocation pairs is created. The local portion of the database favors thelocal user. The union of all the local portions of the location databasedesirably constitutes the name space of the entire MPSM system of thisinvention. It is understood that any of the many database managementsystems or storage schemes known in the art can serve as the platformfor this location database. Thus, location names can be provided withoutthe need to rely on a global positioning system, reducing batteryconsumption. Location data can additionally or alternatively bepurchased or otherwise provided by a third party.

An additional and/or alternative approach for automatic locationdetermination relies on WiFi triangulations. Mobile devices can grow andmaintain a database of known open WiFi networks, for clarity we callthis database an Open-WiFi-Net database. Such mobile devices can use theinformation stored or derived from the information stored in theOpen-WiFi-Net database to further refine the accuracy of a locationwithout the use of GPS. Via point triangulation, when an Open-WiFi-Netdatabase is available, the mobile operating system uses not only thecell tower but also WiFi triangulations to determine location. It iswithin the scope of this invention to use either or both cell-phone andWiFi triangulations to enhance location information in addition to anyother disclosed approach. The mobile device can use the WiFi signal at adestination, such as destination 43, and additionally or alternativelyany detectable open WiFi signal from a neighboring location, such asestablishment 45 that is adjacent destination 43.

Having created the location database, searching, namely querying, thedatabase uses the cell coordinate or the location name. That is, alocation name query takes a location name as input and returns thecorresponding cell coordinate. A cell coordinate query takes a locationname as input and returns the corresponding location name. Note that,multiple names can be attributed to a given cell coordinate. That is, alocal user might name a location using multiple different names;different users can name same locations using different names.Similarly, the same name can be used for different cell coordinatelocations. All names corresponding to a given cell coordinate arereturned. It is within the scope of this invention to selectively returnnames based on context, user, or community bias. Similarly, all cellcoordinates corresponding to a given name are returned. Again, it iswithin the scope of this invention to selectively return coordinatesbased on context, user, or community bias. Ranking of the resultsreturned can, when desired, be biased towards the local user.

A key concern for MPSM systems is collecting location information.Clearly any location information available within the mobile deviceshould be harnessed. Thus, if GPS readings or any other locationinformation is generated by other device resident applications, thesereadings are desirably recorded and utilized by the method andapplication of this invention. However, reliance on strictly otherapplications to obtain positional information is obviously not realisticor possible.

In embodiments of this invention, positional information is obtained viathe use of geofences. A geofence is geographical boundary or “fence”surrounding a positional reading. As these boundaries are radius based,geofences are generally circular. Location transmission occurs whenevera handover of one cell tower to another occurs and is expected but notguaranteed to occur once a geofence boundary is crossed. To tracklocation, periodic location transmissions are required. Since locationtransmissions must be minimized to conserve device energy, transmissionsshould only occur given geographical movement. Thus, crossing a geofenceshould generate such a transmission. Unfortunately, as crossing ageofence does not guarantee a location transmission, increasing thelikelihood of a transmission is necessary.

In contrast to the known uses that surround a location with a singlegeofence, to increase the likelihood of a location transmission duringmovement, embodiments of this invention include surrounding a locationgeofence with a plurality of geofences. In one embodiment of thisinvention, a method of tracking a user includes determining a locationof the mobile user, automatically establishing a first geofence aroundthe location, and automatically establishing a plurality of additionalgeofences around the first geofence, with each geofence including aboundary. A location transmission is obtained by the mobile device uponcrossing a boundary of the first geofence or any of the plurality ofadditional geofences. Multiple neighboring geofences are advantageoussince they increase the likelihood of a location transmission as theirboundaries are likewise likely to be crossed given movement.

FIG. 2 representatively illustrates a geofence 60 surrounding a currentlocation 62. The geofence 60 is surrounded by additional geofences 64,all within a given cellular tower transmissions cell 65. Note that partof a neighboring geofence 64′ is not fully within the cell 65, andhence, limits its benefits since a cell tower handoff by movement intocell 65′ will generate a location transmission.

Geofences are implemented as software processes. Operating systems formobile devices, such as but not limited to iOS and Android, limit thenumber of processes available to an application, and thus, the number ofgeofences is bounded. However, this limit typically exceeds the numberof geofences generated using the approach described above. Therefore,additional processes are available, and hence, additional geofences arepossible.

To increase the likelihood of a location transmission given movement, inembodiments of the invention, the remaining available processesimplement static geofences. A static geofence is not dynamicallygenerated given a new location as previously described. Rather, a staticgeofence is one that is fixed and represents those locations that arelikely to be crossed by a given user. That is, users are habitual andfrequent a limited set of locations often, for example but not limitedto, their home, office, or favorite wine or sushi bar. By learning thefrequent locations of users both individually and system wide andsetting static geofences at these locations, biasing by the individualuser, the probability of a location transmission is increased sinceadditional geofences are likely crossed.

More so, these repeated locations vary by city, county, state, country,etc., as well as by other factors such as but not limited to day andtime. Geographical and temporal presence can thus be used to vary theset of static geofences for a given user. For example, the set of staticgeofences for a given user will vary if the user is in Washington, D.C.rather than in San Francisco, Calif. Similarly, the set of staticgeofences for a given user will vary depending on the day and time. Forexample, a user frequents work on weekday mornings but frequents theirfavorite bagel shop on Sunday mornings and their favorite sushi bar onThursday evenings.

Location transmissions suffer from a margin of error. Thus, it isdifficult to precisely pinpoint and tag a location with a singletransmission. Embodiments of this invention include automatic refiningof a location of a user destination as a function of user routines, suchas established by several user visits to the destination. As timeprogresses however, and a user frequents the same location multipletimes, multiple location transmissions for the same location arerecorded. In one embodiment of this invention, as representatively shownin FIG. 3, by overlapping the transmitted location along with its marginof error, a more accurate location can be derived. The overlapping oflocation transmissions for a given location 70 between streets 72 andwithin geofence 74, along with their margin of errors, represented ascircles 76, yields an accurate location placement.

As shown in FIG. 3, location accuracy improves as related data arecollected. Related data, however, can, at times, be somewhat erroneous(in terms of accuracy). A non-limiting example is an entrance to ashopping mall. Such an entrance is not necessarily at the center of thecomplex. Regardless of the entrance displacement from the center of thecomplex, the entrance location can still be used to increase locationaccuracy of the mall complex since the readings for the entrance areconsistent. That is, for a given user, given mobile device, givencarrier, etc., such location recordings remain consistent, all be it,slightly erroneous. Thus, even dirty, namely potentially inaccurate,data can result in correct location identification.

Additionally, having established a location, corresponding lat-long paircoordinates can be reversed engineered, namely mapped back onto, a placename. These derived lat-long pair coordinates become yet an additionalinformation component that is used by a learning system to better refinea mapping to a named place. Machine learning, data mining, andstatistical approaches that are supervised, semi-supervised, orunsupervised can be used, as known in the art, to cross-correlate allavailable location related data.

Once determined, the user information including the location and/or theuser activities are automatically stored in a database. Embodiments ofthis invention include a computer server for providing and implementingthe tracking and/or social networking service of this invention. Thecomputer server includes a location module to determine the userlocation and/or a tagging module configured to correlate manuallyentered user information to a user destination and a database moduleconfigured to store user information including user locations and useractivities at the user locations. For social media sharing, the serverfurther desirably includes a communication module configured toautomatically share a user activity in the social networking serviceupon further user arrivals at a corresponding one of the user orcommunity locations. The server can also include an association moduleconfigured to associate the user activity with the corresponding userlocation.

Since location transmissions are needed during movement, the obviousquestion arises: when should the transmissions cease? That is, thesystem must determine when the user has arrived at a location to knowwhen to perform the automatic steps discussed above. As discussed above,GPS systems are an energy drain on a mobile device, particularly as theGPS remains on and linked with the satellites to maintain locationdetection. Keeping a GPS application operating is a drain on both theprocessor and the battery of the mobile device. This invention providesa method and executable application that conserves energy by notcontinually running during use of the mobile device.

Embodiments of this invention provide an automated method of tracking amobile user that includes providing a location module configured toreceive location transmissions, placing the location module into a sleepmode, awakening the location module upon receipt of a locationtransmission, and determining a location with the location module. Theseplacing, awakening, and determining steps are repeated, thereby placingthe application into a sleep mode when not needed, thereby reducing thedrain on the mobile device. The application goes into sleep mode whennecessary or when desired, such as when the application is not needed,e.g., during extended movement or upon an arrival at a location. Inembodiments of this invention, the application can go into sleep modewhenever a time since the device awakening exceeds a predetermined timeallocation, or upon a determined rate of travel exceeding apredetermined threshold, thereby indicating extended travel.

FIG. 4 illustrates one exemplary, and non-limiting, method according toan embodiment of this invention to automatically detect arrival at adestination. The method is useful for tracking a user's location for anyof various reasons, including, for example, monitoring communitymembers, such as for triggering events, for safety, to provide automatedreminders, and/or to provide automated suggestions to the user basedupon the destination and/or surrounding area. The method of FIG. 4 isparticularly useful for implementing the method and system discussedabove, and can be used to implement other applications and method toprovide energy savings compared to GPS location methods in mobiledevices.

FIG. 4 includes a flow chart 100 that includes and/or represents threedistinct situations, namely, an actual arrival, rapid movement, andsporadic movement without an actual arrival. Initially, the applicationis in sleep mode. Sleep mode is a state when no processing, and hence noenergy consumption, takes place. Processing occurs once the applicationis awoken. A location transmission, such as a cell tower transmission oranother application obtaining location information, awakens theapplication in step 102. Since the application awakening occurs due to alocation transmission, the current location is known.

Once awakened, the application typically has a maximum amount of time tocomplete its processing. This limit, called time allotment, is set bythe device operating system. All processing must complete prior toexceeding the time allotment. Ideally, the application should relinquishthe processing flag back to the device operating system before theoperating system forcefully removes the application from its activequeue. Voluntarily terminating an application, namely returning it tothe sleep mode, rather than having it forcefully terminated by the hostoperating system, is consider good citizenship. In step 104, theapplication initializes two timers, namely, a timer count representingthe duration of time the process has executed since last awakening, anda stationary count representing the duration of time since the lastdetected device movement.

As time progresses and the process executes, the timer count isincremented in step 106. In one embodiment of this invention, wheneverthe application processing time exceeds the operating system timeallocation (108—YES branch), the application is voluntarily placed insleep mode 105. Note that the time allocation threshold is notnecessary, but set to support good citizenship.

Assuming that the time limit has not been reached (108—NO branch), theapplication waits for t time units in step 110. After waiting t timeunits, new current location data are obtained is step 112 and storedlocally on the device in step 114. In step 116, the current location iscompared to the previously known location. If the two locations differ(116—NO branch), the rate of travel is computed in 118. If the rate oftravel exceeds a threshold (120—YES branch), the process is desirablyand voluntarily placed in sleep mode 122. Rapid travel is unlikely toresult in an immediate or near term arrival; thus, checking locationswhile moving rapidly unnecessarily uses device energy. Eventually, theapplication process is awoken with the device moving at a slower rate.At that time, location checking is needed as an arrival might soonoccur. If or when the rate of travel is slow (120—NO branch), movementis noted in step 124, and the loop is repeated commencing with theindication that additional processing time has elapsed in step 106.

Thus far, the arrival detection process has been voluntarily placed insleep mode either due to having exceeded the self-imposed processingallotment quota which is desirably set just slightly below the operatingsystem's time limit that leads to the removal of the application fromthe active queue (108—YES branch) or having travelled too rapidly(120—YES branch). Slow travel has resulted in simply recording thelocations traveled, noting the movement exists in step 124, and awaitingeither arrival or process termination.

Arrival is determined when the same location is detected for asufficient duration of time. That is, an arrival is potentiallydetermined when the location remains the same (116—YES branch). Thestationary detection count is then incremented in step 126. If thestationary threshold is not yet exceeded (128—NO branch), theapplication waits fort time units in step 110, and the current locationis obtained in step 112 and stored locally in step 114. A sufficient andpredetermined duration at the same location eventually surpass thearrival detection threshold (128—YES Branch).

Once arrival is determined, arrival is declared in step 130, all dataregarding the prior locations visited and stored locally are compressedand sent to the back end system supporting the application in step 132.A new location checkpoint is established in step 134, and the process isplaced in sleep mode 136. From the sleep modes, the process of FIG. 4repeats upon a known location.

Compression of location data is typically performed prior to localdevice to back-end system transmission as often the location data maynot be needed at the back end. Location data may not be needed in cases,for example but not limited, during rapid travel. Although exemplifiedas having data compression occur prior to the sending of the data to theback-end, it is within the scope of this invention to compress locationdata prior to storing them locally.

All parameters described above for FIG. 4, for example t (for the timeunits), timer count, etc., are system and device dependent.Experimentation with and fine tuning of these and other parameters iswithin the scope of this invention. Also within the scope of thisinvention is the tuning of these and other parameters via the use ofmachine learning, data mining, and statistical approaches; supervised,semi-supervised, and unsupervised approaches can be used.

As discussed above, once the user has arrived at a destination, thelocation identification, user activities at the location, and/or anyproximate third party members of the user's community are determined, ifnot already known. In this way, the devices automatically continuallydetermine locations which can be used to identify any establishmentsand/or any community members at or within proximity to the location.

User activities are actions or events. Example user activities includebut are not limited to “drinking wine,” “flying,” “reading,” “attendingconference,” or “commuting.” Users specify a particular user activityeither by selecting from a provided list or by entering a different useractivity. As discussed above, the provided list is generated by storingall previously entered user activities from all systems users butbiasing the ranking of the provided activities based on context, thelocal user, their community, or a combination thereof.

All location and user activity pairs are stored in a databasecorrelating the location with the activity. Any of the many databasemanagement systems or storage schemes known in the art can serve as theplatform for this location-activity database. Furthermore, it is wellunderstood in the art that the location-activity database can store manyadditional features. For example, the user identity and date and time ofthe pair are recorded.

Over time, the database grows and contains a sufficient number of pairsto support mining. The volume of data needed to mine correlations isdependent on the mining algorithm deployed and the level of accuracyneeded. As known in the art, there are many machine learning, datamining, and statistical approaches to support mining. By using any ofthe many available such approaches, either individually or incombination, a local user activity preference per location is learned.Example learning approaches include supervised, semi-supervised, andunsupervised approaches including but not limited to Naïve Bayes, NeuralNetworks, Support Vector Machine, and Associating Mining basedtechniques. The use of proprietary mining techniques is likewise withinthe scope of this invention. Once local user preference is learned, thispreference is used to bias the aforementioned provided user activitylist.

There are many approaches to identify locations. Automated locationidentification is accomplished by periodic checking of the currentlocation. Periodicity of checking depends on, for example, the methodused to determine the location, the desired frequency of reporting,recording, and notification, and the resources available to support thechecking. Other periodicity determination approaches known in the artcan likewise be used. One approach to automate location identificationis the periodic determination of lat-long pairs via the use of a GPSdevice. An online service or a locally resident database can be used tocorrelate the GPS readings with locations. A preferred embodiment ofthis invention uses the aforementioned location database. Whenever atransmission to a connected cell tower is made, the cell coordinates ofthe transmitting device are used as a search query against the locationdatabase. If a match is detected, that location is identified. Anotherpreferred embodiment detects locations upon the crossing of geofenceboundaries as previously discussed. Note that both dynamicallydetermined geofence boundaries and static geofence boundaries detect alocation. Yet another preferred embodiment detects locations bycapitalizing on location transmissions generated by any otherapplication operating on the mobile device requesting locationinformation.

In embodiments of this invention, local users, unless disabled by alocal user, can be provided with automated notifications for themselvesand for their community members. These notifications describe locations,activities, or correlated locations and activities for themselves andtheir community members. For example, unless disabled by the user, anytime a user arrives at a new location, the local user and theircommunities can be notified of the user's new location. Automatedlocation detection and notification, unless disabled, occurs withoutrequiring a local user prompt.

Similarly, activity notification can be automated. Once a user arrivesat a location, a set of activities previously occurring at that locationis shared with the community or provided to the local user forinformation or sharing. If the user chooses to confirm at least one ofthese past activities, both the local user and their respectivecommunity members are notified of this at least one activity.

In another embodiment of this invention, automated notification involvesshared experiences. A shared experience is one that associates multipleusers. These associations can be passive or active. A passiveassociation is strictly informative in nature while an activeassociation requests an action. Non-limiting examples of passive sharedexperiences based on locations include: “User A is at User A's office,as is User B” and “User A is at home as is User C.” Note that the firstexample involves multiple users at the same physical location, namelyUser A's office, while the second example involves multiple users at thesame relative locations, namely their homes, but at different physicallocations.

Similarly, passive shared experience notifications can be based on useractivity. Non-limiting examples of passive shared experiences based onactivity include: “User A is eating lunch as is User B” and “User A isparticipating in her favorite sport as is User B.” Note that the firstexample involves multiple users participating in the same activity,namely eating lunch, while the second example involves multiple usersinvolved in similar nature of activities, namely participating in theirown favorite sport, which can be different actual activities, namelyracquetball and swimming. In both passive shared experiences based onlocation and on activity, known in the art machine learning, datamining, and statistical approaches that are supervised, semi-supervised,or unsupervised approaches can be used to correlate relative locationsand activities to physical locations and activities.

Other shared experiences can prompt for action, and are thus consideredactive. A non-limiting example of an active shared experience promptingfor action includes: “User A posted a picture when at Penn Station; youare now at Penn Station; please post a better picture?” Thus, activeshared experiences request the user to actively react. As above, activeshared experiences can be location or activity based and can be absoluteor relative. Note that it is likewise within the scope of this inventionthat individual user notifications be active and passive, in a similarmanner as described above. However, the correlation of locations andactivities both for passive and active are based strictly on thecurrent, past, or projected expected activities of the individual userrather than those of multiple users.

Typically, only changed locations and activities are notified. That is,a location or activity is not typically repeatedly identified. However,a local user can request repetitive notifications based on anytriggering condition known in the art.

Local users do not always remember to indicate a new location name orconfirm which of the possible suggested name or names the systemindicated for the given the location. As such, it is at timesadvantageous to prompt the local user for information. However, overlyaggressive prompting might annoy the user. In embodiments of thisinvention, the application non-invasively prompts the user upondetecting an unknown location for the given local user. To avoidannoyance, prompting is repeated only rarely, say twice; the number ofrepeated prompts can be set as a parameter. Similarly, to provide asense of comfort, if the back-end system recognizes the location basedon the local user's community members' naming schemes, it prompts thelocal user with guiding messages, for example but not limited to “Manyof your community members call this location The Tasting Room”.

Identification of activities associated with a given location or a givencommunity member can be additionally or alternatively automaticallyinferred in multiple ways. In embodiments of this invention, thecomputer system can automatically determine a positional destination ofa user, such as by using a mobile device discussed herein, andautomatically deduce as user information a location type and/or useractivity of the positional destination. The user information can bededuced, at least in part, based upon the destination context. Exemplarycontext information includes, without limitation, time-dependentinformation (e.g., what time of day is it?), community information(e.g., who is also there?), and/or third-party information about thepositional destination. This method, tied with automatic sharing of theuser information in a social networking service, can provide a partiallyor fully automated process for determining user location and activity.

In one embodiment of this invention, the automatic deducing of the userinformation is based upon known or learned user routine. As discussedabove, local users typically follow standard routines. Some routines aredaily, weekly, monthly, etc. Other routines are context dependent.Regardless of the nature of the routine, learning via any of the manystatistical, machine learning, data mining, or business analyticaltechniques known in the art, enables predictive behavior and automatedactivity and location suggestion. For example, but not limited to, if alocal user always goes out to lunch at noon on every weekday, then if anunknown location is detected on a Tuesday at noon, then the applicationcan suggest that this unknown location is likely a restaurant and theactivity is likely eating lunch. Similarly, routine identificationenables the prevention of transmissions both positional andinformational. For example, but not limited to, if a local user alwaysgoes to sleep at midnight on Sunday through Thursday and awakens at 7:00am the following day, then energy can be saved if the applicationvoluntarily places itself in sleep mode during the hours that the localuser is known to be sleeping. Additionally, routines can involve asequence of activities and locations. A non-limiting example of asequence of activities includes: On weekdays, Eric arrives at his officeat 8:00 am, drinks coffee at 10:00, develops software from 11:00 amuntil 5:00 pm, commutes home at 5:30, and finally, arrives at home at6:00 pm.

Another location and/or activity deduction approach is by association.The automated deducing can include automatically associating a user witha second user at a positional destination. If the second user's locationand/or second user's activity is known, then the system canautomatically infer the location type and/or user activity of the firstuser from the second user location and/or activity. Consider a previousknown event such as: “Community member Sally swimming at the Somersetpool”, assuming that the Somerset pool location was previouslyidentified. As an example of automatically determining a currentactivity of community user Sam, the system identifies through locationdetermination that Sam is currently at the same location as Sally, andalso that Sally is currently at the Somerset pool. From thisinformation, possible automatically postulated associations andactivities are: “Sam is at the Somerset pool”, “Sally is swimming”, and“Sam is swimming”. Thus, it is possible to infer an activity for acommunity member from association with another community member. It iswithin the scope of this invention to use any logical inference methodsknown in the art to generate plausible associations. It is also withinthe scope of this invention to obtain confirmation of the plausiblepostulated activity by the community member, in this case Sam, by askingeither Sam or Sally or by any other means known in the art.

Desirably the computer system operating the MPSM automatically storespast user information, including past location type and/or user activityof the positional destinations of all users. User information for futurevisits to repeat positional destinations can be automatically deduced asa function of the stored past location type and/or user activity of thepositional destination. In embodiments of this invention, the system canrely on recorded previous activities of a user, a community member, orany system user at a given location to postulate on a user's activity ata given location. Past context information for past visits to thepositional destination by the user and/or community members of the usercan be compared to a current context of the user's visit to thepositional destination to deduce the user information. In oneembodiment, the system can reduce possible location types and/or useractivities as a function of the past location type and/or user activityof the positional destination.

As an example, at a given Location A, users previously studied, talked,ate, and drank. Thus, if a user's positional destination is detected asat Location A, then plausible activities postulated can be studying,talking, eating, and drinking. More so, if the given user's communitymembers only previously talked, ate, and drank, it is with a higherprobability to postulate that the given user is talking, eating, anddrinking rather than studying. Furthermore, if the given user visitedLocation A previously, but only talked and drank, then an even higherprobability is that the user is currently talking and drinking ratherthan eating and studying. It is within the scope of this invention topostulate some or all of the previously detected activities of a givenlocation. More so, it is within the scope of this invention to rankorder the activity suggestions according to the relevance of thepreviously visiting users to the given current user. As previouslydescribed, the system can request confirmation of suggested activitiesthrough the user's mobile device.

The system can additionally or alternatively reduce possible locationtypes and/or user activities as a function of the past location typeand/or user activity of the positional destination as a function of thetime of day. The system can rank possible location types and/or useractivities of the positional destination based upon known past timeperiods corresponding to the time of day of the current user visit. Forexample, again given Location A, if previous visiting users wererecorded to study one or more times during the intervals: 3:00-4:30 PMand 7:30-9:00 PM, and to drink one or more times during the intervals:4:00-7:00 PM and 8:30 PM-2:00 AM, then a current visiting user atLocation A at 3:15 PM is likely studying, at 4:15 PM is likely to beeither studying or drinking, and at 1:00 AM is likely to be drinking.More so, if the given user's community members only studied between3:15-4:30 PM then it is with a higher probability to postulate that thegiven user is studying rather than drinking at 4:15 PM. Furthermore, ifthe given user visited Location A previously but only studied, then aneven higher probability is that the user when at Location A is studying.It is within the scope of this invention to postulate some or all of thepreviously detected activities of a given location. More so, it iswithin the scope of this invention to rank order the activitysuggestions according to the relevance of the previously visiting usersto the given current user. As previously described, the system canrequest confirmation of suggested activities through the user's mobiledevice.

In embodiments of this invention, time context alone can be used topostulate activities. For example, if most days, a user is recorded tobe drinking coffee between 9:00-10:00 AM, then, without contradictoryinformation, a plausible activity postulate is that at 9:35, the user'sactivity is drinking coffee. Again, as previously disclosed, it iswithin the scope of this invention to rank order the postulated activitysuggestions according to the relevance of the previous users to thegiven current user and/or to obtain confirmation of suggestedactivities.

Additionally, it is also within the scope of this invention to rankorder the time postulates based on frequency of occurrence within thetime interval. This rank ordering applies to both location based andlocation independent time based postulates. For example, if in theinterval 4:00-4:30 PM, community members studied 25 times but drank 5times then, at 4:15, it is with a higher probability to postulate thatthe given user is studying rather than drinking.

In embodiments of this invention, the system can search and/or use, ifavailable, external, third party information about the positionaldestination for postulating activities for a given location. Forexample, third party vendors might provide, either free of charge or fora fee, activity information for a given site. Consider a marketingwebsite of a centralized homepage for a grocery store chain. Suchwebsites are likely to contain addresses of many or all of theassociated stores. Since these stores all support shopping, an activityassociated with these locations is shopping. Similar information can bederived or purchased from other sources such as but not limited tocommercial information repositories. Additionally, maps can be parsed.Given a location of a road, an activity of that location is likely to bedriving. Various and alternative third party information gatheringapproaches and their incorporation into activity classification andpostulation can be incorporated into the method and system of thisinvention.

Suggested activity information, particularly but not limited toinformation obtained or derived from third party vendors, might beadditive or might be contradictory. Thus, combining or reconcilingpotential activities is needed. The use of voting schemes, biased basedon credibility of the source or on frequency, such as majority, or otherknown techniques, can be incorporated in the method and system of thisinvention. Note that differing suggested plausible activities mayadditive or may be contradictory. The use of techniques such as, but notlimited to, conflict resolution methods, ontology determination, andclustering, etc., can be incorporated to recognize potential conflictsand to expand classification is within the scope of this invention.

Additionally, the classification of plausible activities based onactivities occurring in the surrounding vicinity is likewise within thescope of this invention. For example, consider an unknown locationadjacent to two known locations, such as, but not limited to, twoneighboring stores or two neighboring beaches. For the neighboringstores, known activities might include shopping and strolling, while forthe neighboring beaches, known activities might include sunbathing andswimming. Given location proximity, it is within the scope of thisinvention to suggest a user's activity at the unknown location to beeither shopping and strolling or sunbathing and swimming, respectively.Confirmation can always be obtained for suggested activities and to biassuggested activities based on user familiarity and frequency ofoccurrence.

In embodiments of this invention, local users can opt to delay theirnotifications. That is, once a location is visited or an activityoccurs, a local user can opt to have the notification of the location oractivity be delayed by some period of time. Delaying a notificationprovides the local user with the ability to notify their community ofthe location visit or activity occurrence, but still provides the localuser time to progress to the next location or activity.

Notifications can be complemented with correlations with other communitymembers. That is, both the local user and their respective community canbe automatically notified with a comparison. A comparison, for examplebut not limited to, can identify other community members havingpreviously conducted a specific activity or having visited a givenlocation previously. Comparisons are made by checking other communitymember locations and activities against those of the local user.Checking is performed via a search of the location-activity database. Ifa match exists within a specified or predetermined period of time, acomparison notification is made automatically. The period of time can bearbitrarily set or can follow some logical time quantum such as hour,day, week, etc.

Locations and activities are known by name. However, in addition to aname, locations and activities can have associated personal labels.Labeling locations and activities can detail familiarity to the locationand activity. User labels for locations can be surrogate names, forexample, “favorite city” for Chicago, can be songs or sound waves, forexample song words “my kind of town, Chicago is” for Chicago, can be apicture, for example “the Water Tower” for Chicago, can be a video, forexample “a panoramic view of the Chicago skyline” for Chicago, or anycombination of these and other multimedia tags supported by the localdevice. Similarly, user labels can exist for activities. For example,“favorite vice” for drinking wine, or it can be a song or sound wave,for example the song words “a bottle of red” for drinking wine, or itcan be a picture, for example, a wine bottle picture for drinking wine,or it can be a video, for example “a panoramic view of a vineyard” fordrinking wine, or any combination of these and other multimedia tagginglabels supported by the local device.

In embodiments of this invention, local users and community members cancomment on their own and each other's locations and activities. Commentscan take any of the many multimedia forms provided by the local device.These include, but are not limited to, text, sound, pictures, videos, orany combination thereof. Multiple comments can be made by the localuser, their community, or combination thereof. In addition to statingtheir opinions (commenting), community members can prompt forclarification. That is, by issuing “what” comments, community membersrequest additional information on the posted locations and activities.Additionally, user can “like” their own and each other's locations andactivities. By “liking” a location or activity, community membersexpress their satisfaction of their respective community members'presence in terms of location and activity. Multiple community membersas well as the local user can “like” a location and activity.

The method and systems of this invention can track vast data on both thelocal user and their respective community members. These data cover,including but not limited to, locations, activities, and alsoindividuals both who are system users and those who are not. These datacan be stored and summarized. A summary of the local user and communitymember locations, activities, time durations involved in each of theselocations and activities, individuals who they encountered, etc., can becomputed and presented to the user. This summarization can range fromsimple statistical aggregation to advanced correlations as derived byany of the many, both individually and combined, machine learning, datamining, business analytics, and statistical techniques known in the art.

Information that can be aggregated or derived can answer, exemplary butnot limiting, questions such as: how much time a local user spent doingthings, such as, working at home, working out, walking the dog,commuting to work; how much time a particular community member spentdoing things, such as, working at home, working out, walking the dog,commuting to work? (Note that the information derived for the communitymember is based strictly on the information that that particularcommunity member chose to share); who are the five most commonindividuals that a particular user interacts with?; what is thelikelihood that after seeing a particular user, the given local userwould see a particular different individual?; which activities andlocations are most closely associated with each other and when are theymost likely to occur?; which three users among a given community aremost likely to visit a particular location together?

Local users can be provided with summaries of their locations, durationsat these locations, and activities at these locations. Furthermore, atthe discretion of the local user, these summaries are made available totheir community members.

The system can also generate and maintain both aggregation and derivedinformation. This information can be used to optimize suggestions toavoid obstacles, for example, but not limited to preferred routing ofcommuting path, promoted target advertising, for example but not limitedto location of nearby ice cream store for those users who frequentlyrecord “eating ice cream” as an activity, and a host of otherinformational services known in the art.

The invention includes a method of determining infection risks and/ormedical conditions of users participating in a social networking serviceusing the above described monitoring and learning method and system.From the automated monitoring of destinations and user activitiesdescribed above, the computer system learns activity patterns for users.In embodiments of this invention, risk metric values assigned tolocations and activities of the user and/or deviations in activitypatterns can be determined and analyzed for possible indicators ofhealth concerns.

Embodiments of risk assessment in this invention further include aconsideration or time at, or other persons involved at, the locationand/or activity of the user. For example, the method can include adetermination of a corresponding user participation time for the each ofthe locations and/or activities, and scaling the risk metric for theeach of the locations and/or activities according to the correspondinguser participation time. If the user is in a high-risk area for 2 days,then the risk assessment is higher than for someone who simply drovethrough the area without stopping. Likewise, going to a restaurant forone hour of dining is riskier than for 5 minutes for take-out.

The risk metric for each of the locations and/or activities can beassigned by any suitable scaling. For example, FIG. 5 shows an exemplarytable of infection rates for various areas in Maryland, which can beused to provide a location risk factor. For example, for each of thelocations, the assessment score can be normalized by the number ofinfected people in an area around the location. FIG. 6 shows arepresentative activity scale, such as are currently provided bygovernment and/or healthcare agencies for mitigating Covid-19. Thescales can be fixed or fluid, as needed, and can vary depending on thepathogen. The risk metrics of the user risk assessment can be adjusted(e.g., increased) upon an in-person contact at the locations and/oractivities, particularly if the contact person is known to have anegative risk assessment based upon, for example, the factors andmethods described herein. Other known information, such as a person'semployment situation or general health condition, can be taken intoconsideration as well. For example, is a user or a contact person an‘essential worker’ who is on the job, or working from home?

Using the aforementioned measures, for example, embodiments of thisinvention determine a categorical degree of risk of infection, forexample, low, medium and high, that visiting locations, performingactivities, and/or physically interacting with a particular communitymember poses based on that member's prior geographical presence andbehavioral patterns. By identifying the degree of disease prevalence atthe geographical location resided or visited by a community member andthe risks associated with that community member's activities, a scoringof infection likelihood can be computed. That score can be computedusing any metric known in the art that incorporates the aforementionedmeasures. An exemplary computation, without limitation, is as follows:

User Risk Assessment—Warning Factor (W)

1. Location metric

-   -   Normalize the number of infected people for 100,000    -   (L)=Rate-per-100K/Scale-Rate

2. Activity metric

-   -   Normalize accepted activity “risk factors”    -   (R)=Risk-Factors/Scale-Risk

3. Duration metric

-   -   Normalize the duration per time unit    -   (D)=Duration-in-Minutes/Scale-Time-Unit

4. W=f(L, R, D), for example:

$W = {\sum\limits_{{all}\mspace{14mu}{activities}}\left( {\frac{{Rate}\text{-}{per}\text{-}100\; K}{{Scale}\text{-}{Rate}}*\frac{{Risk}\text{-}{Factors}}{{Scale}\text{-}{Risk}}*\frac{{Duration}\text{-}{in}\text{-}{Minutes}}{{Scale}\text{-}{Time}\text{-}{Unit}}} \right)}$

By categorization of the computed score, each member can be assigned arisk category indicating the likelihood that the member might beinfected. As an example, a possible hypothetical categorization is thatscores below 10, between and including 10 and 100, and above 100 arelabeled low, medium, and high, respectively. Other categorizationschemes with greater or lesser granularity and differing thresholdboundaries are likewise within the scope of this invention. Prior tomeeting any community member, the risk category of the member to be metis provided to the scheduler of the meeting to determine the risksassociated with the potential meeting.

Consider the following non-limiting example used strictly withoutlimitation to illustrate an embodiment of the invention. It is alsowithin the scope of this invention to incorporate other known in the artevaluation metrics and/or database recording organizations that accountfor community interaction, location presence, and date, time, durationand types of activities associated with a given location and communitymember.

Assume Sally (ID #11) is considering meeting outside at a socialdistance with one of three friends: Bob (ID #33), Jim (ID #88) or Mary(ID #99). Of concern to Sally is staying safe, and thus, she considersthe warning factor (W) associated with Bob, Jim, and Mary. Alllocations, durations, and types of activity for every friend underconsideration is obtained from the database records shown in FIG. 7. TheCovid-19 risk assessment scores for activity types are taken from theguidelines shown in FIG. 6. The cumulative Covid-19 cases for all 23Maryland counties and the City of Baltimore are obtained from FIG. 5.Thus, for this hypothetical example:

Bob (ID #33):

-   -   Prince George County; working out at a gym; for 60 minutes    -   Montgomery County; eating at an inside restaurant; for 90        minutes    -   Allegany County; went to a bar; for 150 minutes

Jim (ID #88):

-   -   Calvert County; eating at an outside restaurant; for 120 minutes

Mary (ID #99):

-   -   No recorded activities

Using the above exemplary, the scoring function with an exemplaryscale-rate of 1000, scale-risk of 2, and scale-time-unit of 30, thewarning factors of each friend is as follows.

$W_{Bob} = {{\left( {\frac{5062}{1000}*\frac{8}{2}*\frac{60}{30}} \right) + \left( {\frac{3628}{1000}*\frac{7}{2}*\frac{90}{30}} \right) + \left( {\frac{6088}{1000}*\frac{9}{2}*\frac{150}{30}} \right)} \approx 213}$$W_{Jim} = {\left( {\frac{1993}{1000}*\frac{4}{2}*\frac{120}{30}} \right) \approx 16}$W_(Mary) = 0Thus, using the above categorization, meeting with Bob, Jim, and Maryhas a high, medium, and low risk, respectively.

Although in the example, only activities for Dec. 10, 2020 are includedin the warning factor computations, it is within the scope of thisinvention to include a smaller or larger than a single day windowobservation of activities. That is, any time duration can be used as anactivity window. It is likewise within the scope of the invention torely on infection guidelines to determine the duration length of theactivities observation window, such as a one- to two-week period beforethe meeting, representing any recognized incubation period for apathogen.

It is likewise within the scope of this invention to also account forinteractions with community members. That is, the weighting function canbe expanded to account for interaction with community members. Forexample, the warning factor W can be modified to include a meetingfactor M_(i,j) that indicates the effects of the warning factors W_(i)and W_(j) from the previous activities observation duration for everypairing involving a person being scored.

In embodiments of this invention, deviations in activity patterns can bedetermined and analyzed for possible indicators of health/infectionconcerns. As an example, it is generally known that deviation from dailybehavioral patterns is a potential warning signal for illness. Signs andsymptoms of many illnesses, including Covid-19 include the loss ofability to perform daily activities, appetite, and sleep changes. Thatis, an individual might no longer have interest to participate in formerhobbies or social activities. The individual may exhibit a change ineating habits. Additionally, the individual may sleep more or sufferfrom insomnia. All of these changes in behavioral patterns can beautomatically detected by location and activity aware MPSM technology ofthis invention.

Using the automatically learned behavior in terms of the locality of auser using MPSM technology, a sudden change in locality patterns of anindividual both in terms of locations visited and movement patterns, canindicate a potential early onset of illness, such as from infection.That is, if the individual previously often walked in the morning, andnow does not after a high risk assessment for a time period, this mightbe a cause for alarm. Such deviation in activity is detected, analyzed,and correlated to any related condition using an inference engine usingany of the many well-known in the art pattern detection algorithms thatare part of an MPSM system according to this invention. Additionally, ifthe individual often visited a gym, regularly attended religiousservices, often stopped off at a neighborhood ale house after work, etc.and suddenly fails to visit any of those locations during or after therelevant time period, this too might be an indicator of possibleillness. Again, all of these deviations are automatically detected viathe MPSM technology described herein.

Deviation from automatically learned activities and gatherings can beused to trigger concerns. These too are automatically detected via theMPSM technology according to this invention. Consider an individual userwho although continues with the same locational patterns, deviatessignificantly from their automatically learned activity patterns. Forexample, while an individual may continue to regularly go to theiroffice, their time of arrival and duration of stay at their office mightchange suddenly. Additionally, while at their office, they might havepreviously, regularly eaten lunch with several of their communitymembers and had often taken a coffee break with other community members;if these social meetings suddenly cease to occur, this too can be acause of potential concern and is likewise automatically detected viaMPSM technology according to this invention in the manners previouslydescribed.

As will be appreciated, not every deviation from activity patterns meansillness, as the user may have a reason for the deviation, such as beingon vacation or having a broken leg. Embodiments of this inventioninclude automated analysis of detected deviations to determine thesignificance of the deviation, such as using the inference engine, andcan consider any other user information, before correlating thedeviation to a possible medical condition. As discussed above, thechanges in behavior may be tied to a location, such as no longer eatingwith coworkers despite being at work. If the system detects the user isin Maui instead of at work in Chicago, then not eating with coworkerslikely is temporary, and not of concern. Likewise the system canidentify and/or learn events in the user's life, such as from MPSMinformation according to this invention, and/or identifying words and/orideas from sent or received messages (e.g., e-mail, IM, text, etc.),which may explain a deviation from normal activity, such as “broke myleg” or “tired today from having excessively celebrated last night.”

As discussed above, embodiments of this invention incorporate aninference engine for analysis and/or correlation of deviations. Theinference engine processes real time data feeds and combines them withhistorical information to arrive at fully automated decisions. Thesedecisions take the form of determining whether or not a user isdeviating from normal behavior. Note that regardless of thedetermination, the potential correlation is added to the user's historyso that the inference engine may consider it in the future. Correlationsin a user's behavioral pattern that the system has previously identifiedstrengthen or weaken evidence when appropriate.

In embodiments of this invention, the inference engine identifiescorrelations by using recent methods designed for automatic correlationdetection in big data or data analytic applications, such as but notlimited to the maximal information coefficient. A variety of suchmetrics are computed and considered, so that evidence does not depend ona single metric; correlations supported by a wide variety of metrics aretreated with increased confidence. The inference engine makesrecommendations by considering the probability of a severe conditiongiven the user's history and current state. These probabilities becomemore nuanced or refined as more data become available.

Statistical techniques, machine learning, data mining, pattern detectionand/or any other analytical techniques known in the art can beincorporated as needed to learn users' behavioral patterns. This enablesthe system to improve performance by fine-tuning the alertrecommendation criteria. The system continuously learns normal patternsof behavior for a user and learns how much the user's behavior shoulddeviate before any alarm needs to be triggered. Behavior deviationinformation from a wide, potentially global, set of users is likewiseevaluated to better calibrate the degree of behavioral change of theindividual user that should be noted prior to sounding an alarm. It iswithin the scope of this invention to incorporate outside sources tobetter calibrate this necessary deviation level.

It is also within the scope of this invention for the automated decisionto issue an alarm, to the user or another person, to be based, at leastin part, on the severity of the infection risk and/or the level ofseverity of the ailment associated with the deviation pattern noted.That is, complete loss of sleep is of greater concern than the periodicmissing of meals or the increase in tardiness to meetings. Severedeviations can more readily trigger alarms. Multiple deviations can morereadily trigger alarms. Prolong deviations can more readily triggeralarms.

In embodiments of this invention, when an alarm is triggered, thecondition or conditions triggering the alarm are desirably identified.Also identified can be the duration of the risk assessment behaviorand/or deviation noted and the suspected ailment that might beassociated with the noted deviation.

It is also within the scope of this invention to account for externalfactors that can cause deviation, such as, for example, withoutlimitation, correlations between geographic locations and certainconditions. A user visiting an area with air pollution is less likelythan normal to go for walks. Similarly, when visiting high altitude,activity levels might be reduced. Thus, accordingly, the degree ofdeviation from the normal behavior patterns needed to trigger an alarmmight be changed.

In addition to detection, the disclosed invention supports the automaticnotification of selected community members, specialized healthpractitioners, or alarm monitoring personnel or systems if deviation inbehavior is detected. A set of conditions can be set that trigger anotification, as noted earlier. Either a particular community member orany other predetermined set of individuals or organizations can benotified should a condition be triggered. It is within the scope of thisinvention to notify different sets of individuals or organizationsdepending on differing behavioral changes, hence different triggeringconditions.

It is also within the scope of this invention to have different sets ofconditions to trigger an alarm rather than a single triggeringcondition. Additionally, it is also within the scope of this inventionto have differing triggering conditions depending on the differing riskassessments and/or ailment symptom sets.

It is also within the scope of this invention to weigh the risk metricsand/or the symptoms that are monitored and trigger the alarmdifferently. Symptom or risk weighting can be based on frequency ofoccurrence within a given time period, severity of the indication thatthe symptom or risk represents, or any of the many symptoms, or moregenerally features, weighting schemes known in the art.

In embodiments of the invention the notification sent can depend on userlocality. For example, if the user triggering the alarm is local toChicago, Ill., then community members notified could potentially bethose residing or currently located within a given radius of Chicago.This radius of notification can be based on, without loss of generality,geographic boundaries, population density, number of community membersin the vicinity, or any similar measure. Similarly, with access to thecommunity members' calendars, those members who will potentially be nearthe user triggering the alarm within an upcoming period could benotified of the triggering conditions of the potential ailment. Thetolerated duration of delay to be in contact with the trigger alarm usercan be based on a predetermined period of time or based on the severityof the ailment under consideration.

Additionally, it is also within the scope of this invention to simplyalert the user triggering the alarm of their deviation in behavior asthey might not be aware of this deviation him/herself. It is also withinthe scope of this invention to alert the appropriate healthpractitioners or family members interested in or monitoring thewell-being of the particular user triggering the alarm.

FIGS. 16-22 graphically or otherwise illustrate a simple, exemplarydetermination of activity patterns and deviations, according toembodiments of this invention. Although most of the figures illustratethe monitoring of the top, or most frequent, activities, localities, andinteractions of the user's activity pattern, it is also within the scopeof this invention to monitor any number of any community memberinteractions, activities, or localities.

FIG. 8 illustrates an exemplary summary of top activities determined fora user according to one embodiment of this invention. For each topactivity listed, the corresponding total duration spent on that activityover the covered time period is indicated. The number of top activitiesand the period of activity covered are parameters that can be adjusted.As shown in FIG. 8, the top activities determined for the user duringthe exemplary duration were: working, sleeping, walking, eating, andreading along, with the corresponding total time for each spent over a48 hour period. Specific period ranges can be selected elsewhere. Theactivity granularity corresponds to those activities learned anddetected.

FIG. 9 illustrates an exemplary summary of selected activities acrossmultiple periods. For each of the three activities listed (working,sleeping, and walking), the total duration spent on the activity in eachof the three days (Thursday, Friday, and Saturday) covered is indicated.As shown, the analyzed community member did not work at all on Saturday,but slept significantly more than s/he had on Friday. The granularity ofthe period was set at 24-hours. Periods can be set at any granularityincluding but not limited to hours, days, weeks or months. Parametersindicating the period length, number of periods covered, and number ofactivities are set elsewhere; default parameters can also exist.

FIG. 10 illustrates an exemplary summary of top locations determined fora user. For each top locality listed, the corresponding total durationspent at that locality over the covered time period is indicated. Thenumber of top localities and the period of activity covered areparameters that can be adjusted. Default parameters exist. As seen, thetop localities were: Chevy Chase, Md.; Washington, D.C.; Potomac, Md.;Bethesda, Md.; and Rockville, Md. along with their corresponding totaltime spent at each of these locations over a 24 hour period. Specificperiod ranges are selected elsewhere. The locality granularitycorresponds to those localities learned and detected. This includesgeographic locations as shown as well as relative locations, such ashome or work (not illustrated in FIG. 10, but illustrated in FIG. 11).

FIG. 11 illustrates an exemplary summary of selected localities acrossmultiple periods. For each of the two relative locations listed (homeand work), the total duration spent at that location in each of theseven days (Sunday through Saturday) covered is indicated. As shown, theanalyzed community member was absent from the work location bothSaturday and Sunday; in contrast, s/he spent all day Sunday at home. Thegranularity of the period was set at 24-hours. As previously stated,periods can be set at any granularity including but not limited tohours, days, weeks or months. Parameters indicating the period length,number of periods covered, and number of locations are set elsewhere;default parameters can also exist.

FIG. 12 illustrates an exemplary summary of top determined communitymember interactions with the user being analyzed. For each topinteraction listed, the corresponding total duration spent interactingwith that community member over the covered time period is indicated.The number of top interactions and the period of interaction covered areparameters that can be adjusted. Default parameters are alsoestablished. As seen, the top interactions for the analyzed user were:Nancy Green, Albert Cho, and Eric Jones along with their correspondingtotal time spent with each interaction over a 12 hour period. Specificperiod ranges are selected elsewhere. The community member identitycorresponds to those learned and detected. Although likely that the useranalyzed interacted with both Albert Cho and Eric Jones at the same timefor the same duration, this is not necessarily the case.

FIG. 13 illustrates an exemplary summary of selected community memberinteractions with the user analyzed across multiple periods. For each ofthe three community members listed (Nancy, Albert, and Eric), the totalduration spent with each of them in each of the three days (Saturday,Sunday, and Monday) covered is indicated. As shown, the analyzedcommunity member spent her/his entire Saturday and Sunday with Nancy andpart of Monday with all three of the selected interacting communitymembers. The granularity of the period was set at 24-hours. Aspreviously stated, periods can be set at any granularity including butnot limited to hours, days, weeks or months. Parameters indicating theperiod length, number of periods covered, and the number of locationsare set elsewhere; default parameters can also exist.

FIG. 14 illustrates an exemplary monitoring with a potential deviationconcern detected according to this invention. As shown, the granularitycovered was a week, and five weeks were observed, and three activitieswere analyzed. In terms of walking, in weeks 1 through 3, the observeduser averaged roughly one hour per day; however, in weeks 4 and 5, theobserved user failed to walk any significant amount of time.Correspondingly, the observed user averaged roughly 6 hours per nightsleep in week 1 through week 3, but averaged more than 9 hours per nightin weeks 4 and 5. The duration of time eating likewise increased inweeks 4 and 5. Given a reduction in physical activity, a significantincrease in sleep duration, and an increased appetite, an inferenceengine according to embodiments of this invention is likely to detect apossible onset of illness.

In embodiments of the invention, the MPSM methods and systems can becombined in automated communication with medical devices (e.g., IRthermometers), electronic biosensors and/or other smartphone apps.Exemplary biosensors include without limitation wearable monitors, suchas, pulse, body temperature, fitness, or other electronic body monitors,or non-wearable electronic devices such as scales or fitness machines.FIG. 15 illustrates a smartphone 170 that is a user electronic deviceexecuting the MPSM method according to embodiments of this invention.The smartphone is in wireless communication, such as using Bluetooth® orany of other similar communication protocols, with a wrist-wearabledevice 172, such as a fitness monitor or a smartwatch. The inferenceengine can consider the information about the user gathered by thedevice 172 in detecting, analyzing, or correlating any possibleinfection or deviation according to methods of this invention. Forexample, there are instances of Covid-19 causing pulse irregularities,and/or high blood pressure, in addition to fever.

The invention further includes a method of determining locations and/oractivities of users participating in a social networking service. Themethod can be used to track members and provide warnings to the trackeduser, or to provide a notification or alert/alarm to the user doing thetracking. The tracking and notification are particularly useful inmonitoring health conditions and/or risks to other users. In embodimentsof this invention, locations and/or activities of members of a socialmedia community are automatically and continually tracked, such as usingthe methods described above. The tracked location/activity informationis automatically displayed for each of the plurality of members on oneor more of electronic devices of the other members, depending on theparticular user settings. This can be useful in policing accountabilityin the “bubble” groups friend groups were establishing during pandemicquarantines to have some social interaction while limiting infectionrisk.

In embodiments of this invention, a member has the opportunity toestablish a triggering condition for an other one or more of theplurality of members via her or his electronic device. The trackingsystem automatically monitors the one or more members for the triggeringcondition via the corresponding electronic device(s) of the other user.Upon automatically determining an occurrence of the triggeringcondition, an automated communication of the occurrence of thetriggering condition is sent to the user setting the triggeringcondition, and any other user also added as a notification recipient.This can be used to track children or other family members, and preempta risky activity.

A primary motivator for the use of MPSM systems is maintaining awarenessof the current status of user's community members. Often, maintainingawareness is tantamount to knowing when a community member has met atriggering condition. Examples of a triggering condition include, butare not limited to: proximity to, arrival at, or departure from, aspecified location; and/or an activity or condition of at least one ofthe community members automatically determined using that member'selectronic device of the members, such as having a certain medicalcondition or engaging in a particular user activity or function. Thetriggering condition can be selected from automatically generatedpredetermined condition selections displayed in a user interface on theuser electronic device, or created from predetermined templates providedto and/or created by the user.

Specified locations for the triggering conditions can be absolute orrelative and/or specific or global. As used herein, absolute locationsrefer to specific geographic locations independent of the user, forexample, an address such as 111 Chestnut Street, Chicago, Ill., whilerelative locations specify user related locations such as the user's“home,” “work,” or “favorite coffee shop.” Specific locations can beabsolute or relative and specify an exact location, while globallocations indicate neighborhoods such as Chevy Chase, Md. Thus,triggering conditions based on locations, either absolute or relative,and/or specific or global can be set.

Some medical conditions can be determined by applications available onthe mobile device. For example, applications running on mobile devicesand known in the art include but are not limited to, heart ratemonitoring and temperature sensing. Additionally some medical devicesincorporate state of being applications. Regardless of the means bywhich the medically related condition is determined or tracked,triggering conditions can be specified and another user notifiedautomatically upon the determination of the condition, or a possibilitythereof.

In embodiments of this invention, the MPSM learns and derives useractivities and/or locations, such as by the methods disclosed herein.Triggering conditions related to activities and/or location proximitycan be set by the user or other users. For example, triggeringconditions can be set by a user for the purpose of being automaticallyinformed when a community member is automatically determined to beeating, shopping, or walking. Similarly, triggering conditions can beset for the purpose of being automatically informed when any one or morecommunity member is automatically determined to be near the user, alocation (e.g., restaurant, airport, or mall), or near another communitymember.

Regardless of the triggering condition set, when the community membermeets the condition, the user is notified. In one embodiment of thisinvention, the triggering condition can be set to send the alertnotification to one or more community members in addition to, or as analternative to, the user that sets the triggering condition.Notification can be accomplished by any of the multiple known in the artnotification schemes including but not limited to SMS messages, phonecalls, e-mail messages, and/or application pop-ups or pushnotifications.

Thus, the invention provides a method and system for actively monitoringfor potential medical concerns based location and upon activitydetection and/or deviation and any other health related data from anelectronic device or a related sensing device. The invention is basedupon the methods of this invention for tracking and learning userlocations and activities, and can share concerns with community membersand/or health professionals. The method is executed by a computersystem, preferably through a mobile device, and includes automaticallymonitoring learned user activity patterns.

The invention illustratively disclosed herein suitably may be practicedin the absence of any element, part, step, component, or ingredient thatis not specifically disclosed herein.

While in the foregoing detailed description this invention has beendescribed in relation to certain preferred embodiments thereof, and manydetails have been set forth for purposes of illustration, it will beapparent to those skilled in the art that the invention is susceptibleto additional embodiments and that certain of the details describedherein can be varied considerably without departing from the basicprinciples of the invention.

What is claimed is:
 1. A method of determining infection risks forindividuals during an epidemic or pandemic, such as Covid-19, the methodexecuted by a computer system and comprising: automatically determiningand monitoring positional destinations of a first user using a firstmobile electronic device of the user; automatically determining contextinformation about each of the positional destinations without input bythe first user; automatically deducing as user information a locationtype and/or user activity of each of the positional destinations fromthe context information; automatically learning activity patterns forthe user from the user information; assigning a user risk assessment tothe first user according to locations and/or activities of the userduring a predetermined timeframe; providing the user risk assessment toa community member via a community member electronic device;automatically determining a decrease deviation in the learned activitypatterns during or after the predetermined timeframe from furthermonitoring of further user locations or further user activities;automatically analyzing the decrease deviation to identify asignificance of the deviation, wherein analyzing the decrease deviationcomprises: correlating the deviation to a current location of the firstuser to identify a temporary decrease in the learned activity patternsas a function of the current location not allowing for the learnedactivity patterns, and identifying words and/or ideas from sent orreceived messages via the first mobile electronic device to identify anexplanation for the deviation; automatically correlating thesignificance of the decrease deviation to a possible Covid-19 condition;and automatically alerting the first user via the first mobileelectronic device or a second user via a second electronic device of thepossible Covid-19 condition.
 2. The method of claim 1, furthercomprising providing the user risk assessment to a community memberbefore an in-person meeting with the first user.
 3. The method of claim2, wherein the user risk assessment is automatically displayed to thecommunity member upon an electronic meeting request and/or the meetingbeing entered into an electronic calendar.
 4. The method of claim 1,wherein the predetermined timeframe is at least a predeterminedincubation or latency period of a contagion or pathogen.
 5. The methodof claim 4, wherein the user risk assessment is a compilation of aplurality of risk metrics determined for the locations and/or activitiesduring the predetermined time period.
 6. The method of claim 1, furthercomprising: determining a risk metric for each of the locations and/oractivities for the first user, to provide a plurality of risk metricsduring the predetermined timeframe; and computing the user riskassessment from the plurality of risk metrics.
 7. The method of claim 6,wherein the risk metric is determined from a predetermined assessmentscore for each of the location and any activity performed at thelocation, as a function of time at the location and/or performing theactivity.
 8. The method of claim 6, further comprising: determining acorresponding user participation time for the each of the locationsand/or activities; and scaling the risk metric for the each of thelocations and/or activities according to the corresponding userparticipation time.
 9. The method of claim 6, wherein the determiningthe risk metric for the each of the locations and/or activitiescomprises comparing a location and/or an activity to a predeterminedrisk scale.
 10. The method of claim 6, further comprising increasing arisk metric of the user risk assessment upon an in-person contact at thelocations and/or activities with a person having a negative riskassessment.
 11. The method of claim 1, further comprising: determiningany in-person contact of the user for the each of the locations and/oractivities; obtaining a contact person risk assessment for the in-personcontact; and adjusting the user risk assessment as a function of thecontact person risk assessment.
 12. The method of claim 1, furthercomprising: automatically associating the first user with a second userat the positional destination, wherein a second user location and/orsecond user activity is determined; automatically inferring the locationtype and/or user activity from the second user location and/or activity;and adjusting the user risk assessment due to in-person contact with thesecond user.
 13. The method of claim 1, further comprising for each ofthe locations, determining the assessment score by normalizing thenumber of infected people in an area around the location.
 14. The methodof claim 1, wherein the user risk assessment is a summation of a riskmetric for each of a location, an activity, and a duration of theactivity and/or at the location.
 15. The method of claim 1, wherein thecomputer system comprises the first mobile electronic device in wirelesscommunication with a server computer, and the computer system comprisesmore than one non-transitory recordable medium collectively including aseries of preprogrammed instructions that, when executed by the firstmobile electronic device and the server computer, cause the computersystem to perform the method.
 16. The method of claim 1, wherein theanalyzing the decrease comprises correlating a geographic or weathercondition with the current location, and identifying the temporarychange in the learned activity patterns as based upon the geographiccondition.
 17. The method of claim 1, wherein the user risk assessmentis a compilation of a plurality of risk metrics determined for thelocations and/or activities during the predetermined time period, andeach of the risk metrics is determined from a predetermined assessmentscore for each of the location and any activity performed at thelocation, as a function of time at the location and/or performing theactivity, and further comprising: determining a risk metric for each ofthe locations and/or activities for the user, to provide the pluralityof risk metrics during the predetermined timeframe, wherein thedetermining the risk metric for the each of the locations and/oractivities comprises comparing a location and/or an activity to apredetermined risk scale; determining a corresponding user participationtime for the each of the locations and/or activities; scaling the riskmetric for the each of the locations and/or activities according to thecorresponding user participation time; increasing any risk metric of theuser risk assessment upon an in-person contact at the each of thelocations and/or activities with a person having a negative riskassessment; and computing the user risk assessment from the plurality ofrisk metrics.
 18. A method of determining infection risks forindividuals during an epidemic or pandemic, such as Covid-19, the methodexecuted by a computer system and comprising: automatically monitoringdestinations and user activities performed at the destinations of afirst user via a first electronic device of the first user; receivinguser information comprising a first user activity performed at each ofthe destinations upon a user arriving at the each of the destinations;automatically learning activity patterns for the first user activity byautomatically associating the user information with the at least one ofthe destinations, automatically determining a user activity context forthe first user activity, and automatically comparing a further contextof each of a plurality of further user arrivals to the at least one ofthe destinations to the user activity context, wherein the activitypatterns comprise an eating pattern or an exercise pattern, at alocation or with one or more community members; automatically updatingcontext information associated with the at least one destination fromthe further context of each of the further user arrivals; automaticallyidentifying that the first user is participating in the first useractivity upon future user arrivals at the at least one destination as afunction of comparing the context information to a future context ofeach of the future user arrivals, without receiving any manually enteredconfirmation or manually entered additional user information; assigninga user risk assessment to the user according to locations and/oractivities of the user during a predetermined timeframe, wherein thepredetermined timeframe is at least a predetermined incubation orlatency period of a contagion or pathogen; providing the user riskassessment to a community member via a community member electronicdevice before an in-person meeting with the user; automaticallydetermining a deviation in the learned activity patterns during or afterthe predetermined timeframe from further monitoring of further userlocations or further user activities; automatically analyzing thedeviation to identify a significance of the deviation, wherein analyzingthe deviation comprises: correlating the deviation to a current locationof the first user to identify a temporary change in the learned activitypatterns as a function of the current location not allowing for thefirst user activity at the at least one of the destinations, and/oridentifying words and/or ideas from sent or received messages via thefirst electronic device to identify an explanation for the deviation;automatically correlating the significance of the deviation to apossible Covid-19 condition; and automatically alerting the first uservia the first electronic device or a second user via a second electronicdevice of the possible Covid-19 condition.
 19. The method of claim 18,wherein the user risk assessment is a compilation of a plurality of riskmetrics determined for the locations and/or activities during thepredetermined time period, and each of the risk metrics is determinedfrom a predetermined assessment score for each of the location and anyactivity performed at the location, as a function of time at thelocation and/or performing the activity.
 20. The method of claim 19,further comprising: determining a risk metric for each of the locationsand/or activities for the user, to provide the plurality of risk metricsduring the predetermined timeframe, wherein the determining the riskmetric for the each of the locations and/or activities comprisescomparing a location and/or an activity to a predetermined risk scale;determining a corresponding user participation time for the each of thelocations and/or activities; scaling the risk metric for the each of thelocations and/or activities according to the corresponding userparticipation time; increasing any risk metric of the user riskassessment upon an in-person contact at the each of the locations and/oractivities with a person having a negative risk assessment; andcomputing the user risk assessment from the plurality of risk metrics.